Senior AI Engineer

Charger Logistics Inc.

. Design, develop, and deploy MCP servers exposing domain services as AI-consumable tools with proper authentication, observability, and error handling .

Posted 9/15/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying AI-consumable tools and knowledge retrieval pipelines, with a strong focus on LLM integration and orchestration frameworks. Proficient in cloud platforms and container orchestration, ensuring robust and scalable AI applications.

Highest-signal resume keywords

AI Application DevelopmentLLM IntegrationKnowledge Retrieval PatternsKubernetes DeploymentREST APIs

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLRAGKAGCAGMicroservices ArchitectureAI/ML ConceptsFunction CallingTool UseStructured Outputs

Soft Skills

Strong Communication SkillsTeam Collaboration

Tools & Technologies

OpenAI APIsAnthropic APIsGoogle APIsBigQuerySnowflake

Certifications & Qualifications

Bachelor's in Computer ScienceBachelor's in Artificial Intelligence

Industry Keywords

MCPAgent Orchestration FrameworksKnowledge GraphsStreaming Data Systems

Tech Stack

Tools & technologies

BigQueryCloudKubernetesMicroservicesPythonSQL

About the role

Key responsibilities & impact

Design, develop, and deploy MCP servers exposing domain services as AI-consumable tools with proper authentication, observability, and error handling
Build multi-agent workflows using orchestration frameworks and agent-to-agent communication protocols for complex logistics automation
Develop and optimize knowledge retrieval pipelines using RAG, KAG, and CAG strategies, selecting the right approach based on query complexity, data volatility, and domain reasoning requirements
Design hybrid retrieval architectures that route between CAG for static reference data, RAG for dynamic operational queries, and KAG for multi-hop reasoning across structured domain knowledge
Implement LLM integration layers including prompt engineering, function calling, structured output parsing, and model routing for domain accuracy
Collaborate with cross-functional teams to collect requirements and translate operational workflows into agent capabilities
Deploy and maintain agent infrastructure on Kubernetes with GitOps practices and observability tooling

Requirements

What you’ll need

Minimum 3 years of experience with Bachelor's in Computer Science, Artificial Intelligence, or a related technical field
Strong communication skills and experience working in interdisciplinary or team-based environments
Solid understanding of REST APIs, microservices architecture, and AI/ML concepts
Experience building production-grade AI applications in Python—not just notebooks or prototypes
Hands-on proficiency with LLM integration: function calling, tool use, structured outputs (OpenAI, Anthropic, or Google APIs)
Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation)
Proficiency with SQL and at least one analytical data platform (BigQuery, Snowflake, or similar)
Experience with cloud platforms and container orchestration (Kubernetes)
Background in MCP, agent orchestration frameworks, knowledge graphs, or streaming data systems is a strong asset

Benefits

Comp & perks

Competitive Salary
Healthcare Benefit Package
Career Growth